1. 1 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Developing a Knowledge Management Technology An Encompassing View on the Projects of the Knowledge Management Group at DFKI Kaiserslautern Michael Sintek, Andreas Abecker, Ansgar Bernardi German Research Center for Artificial Intelligence Kaiserslautern, Germany

2. 2 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Overview requirements and approaches to support KM infrastructures for organizations; related research fields KnowMoreactive knowledge supplyfinished Know-Netcollaborationongoing FRODOdistribution, frameworkcurrent MOTIVE3D accessplanned summary: we propose a rich, modular KM middleware as a solid basis for engineering intranet-based KM solutions Development of Knowledge Management technology of the Knowledge Management Group at DFKI Kaiserslautern

3. 3 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Knowledge is an Important Productivity Factor for Organizations besides labor, capital, and land, knowledge has been recognized as an important productivity factor knowledge is stored in individual brains or implicitly encoded and hidden in organizational processes, documents, services, and systems KM is concerned with discovery, acquisition, creation, dissemination, and utilization of knowledge.

4. 4 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Organizations Have Serious Problems in Managing Their Corporate Knowledge Various fields of computer science tackle some of these knowledge problems. Knowledge Problems Documentation Availability Awareness Distribution Resources Multiple Formats Multiple Views Accessibility Discovery Acquisition

5. 5 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Resarch Fields Related to KM Groupware, Workflow, CSCW –collaboration of individuals and departments Document management, retrieval, and filtering systems –most of the available abstract, strategic knowledge written down in text-based documents –often advertised as KM solutions Artificial Intelligence –formal ontologies –data mining –case bases –expert systems We strive for a new quality of knowledge systems by integrating all these areas.

6. 6 Source: MS WET ICE KMN, Stanford, June 16-18 1999 KnowMore—Knowledge Management for Learning Organizations basic research project funded by German government central idea: access to multiple heterogeneous knowledge sources enabled through comprehensive knowledge description using several formal ontologies (information, domain, enterprise ontology) active information delivery integrated into business processes explicit representation of context In KnowMore, knowledge can be viewed as information linked into the application context.

7. 7 Source: MS WET ICE KMN, Stanford, June 16-18 1999 The KnowMore System Architecture

8. 8 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Know-Net—Knowledge Management with Intranet Technologies funded by the European Commission within the “IT for learning and training industry” program integrate groupware functionalities with AI methods enabling the handling of knowledge objects based on Knowledger™ suite (Lotus Notes™ application from Knowledge Associates) and intelligent agents (DFKI) intranet- and agent-based knowledge platform: –codification, mapping, sharing, and reuse of explicit knowledge in multimedia content –corporate knowledge ontologies –intelligent navigation, searching, filtering In addition to a KnowMore-like knowledge platform, collaborative aspects play an important role.

9. 9 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Know-Net: Collaborative Aspects collaborative tools supporting communities of practice at the team level to facilitate the creation of shared memories and interpretative context –real-time group discussions/meetings –project-based bulletin boards and forums –on-line topical conferences with threading features and interactive expertise databases Know-Net mainly exploits the collaboration and coordination technology provided by Lotus Notes and add-on products like Sametime

10. 10 Source: MS WET ICE KMN, Stanford, June 16-18 1999 The Know-Net Intranet- and Agent-Based System Architecture

11. 11 Source: MS WET ICE KMN, Stanford, June 16-18 1999 FRODO—A Scalable OM Framework for Evolutionary Growth (future work) basic research project funded by German government, successor project of KnowMore KnowMore: global set of ontologies, centralized inference FRODO: conjointly use knowledge from several independent knowledge sources –legacy databases –independently introduced partial OMs based on specific ontologies –external knowledge sources (with own ontologies) ontology mapping problem communicating and cooperating services We propose a rich, modular KM middleware as a solid basis for engineering intranet-based KM solutions.

12. 12 Source: MS WET ICE KMN, Stanford, June 16-18 1999 The FRODO KM Middleware Will Exploit Various Notions of Agents digital reference and acquisition librarians –know their respective knowledge source and organization principles –know how to effectively access, search, maintain the knowledge wrappers, mediators, ontologists, knowledge brokers –add intelligent interfaces to legacy systems –make sources accessible to higher-level inferences document analysis and information extraction specialists –allow transition between informal and formal representations task/process agents, knowledge push/pull mechanisms –manage workflow enactment –realize context-sensitive information supply

13. 13 Source: MS WET ICE KMN, Stanford, June 16-18 1999 A Sample Instantiation of the FRODO OM Framework

14. 14 Source: MS WET ICE KMN, Stanford, June 16-18 1999 MOTIVE—Fostering Individual Users’ Motivation for Accessing Online Learning & Training Resources (planned) will be submitted to the EU 5th framework online front-end to electronic learning and training (L&T) systems addresses users’ motivation; important driving factor is social interaction MOTIVE proposes an environment that wraps L&T tools and content together with people’s interactions virtual representation of the L&T environment: –workspace with 3D representation of the organization and of knowledge assets –avatars associated to users –wizard agents with specific roles for promoting available material –support for social processes: events organization, social places (café) etc.

15. 15 Source: MS WET ICE KMN, Stanford, June 16-18 1999 MOTIVE Adds Access to L&T OMs Through 3D Knowledge Portal the L&T contents is accompanied by a KnowMore/FRODO-like knowledge meta-level based upon various ontologies XML as upcoming standard will be used for this knowledge representation task a 3D knowledge portal wraps these ontologies to provide a highly motivating access to the L&T resources thus, the MOTIVE 3D knowledge access can be viewed as an additional, but highly user-friendly information retrieval aspect of the general KM scenario In general, 3D spaces can be used to replace legacy information retrieval, knowledge acquisition, and workflow frontends of OM systems.

16. 16 Source: MS WET ICE KMN, Stanford, June 16-18 1999 Summary In our view, KM technology is a combination of: distributed, heterogeneous knowledge sources various formal ontologies (information, domain, enterprise) knowledge meta-descriptions informal-formal transitions workflow, active support, context collaboration framework, middleware, agents user-friendly access through 3D spaces